A wellness and beauty services and products brand wanted site recommendations in high-priority cities and micro-markets where their stores will perform best with product category-wise split of predicted revenue at each location. The brand already has 200+ existing stores and wants to open the next 100.
The Challenge:
- To identify which cities to prioritize for their next 100 stores
- Further, to identify suitable micro-markets to target within these cities
- Predict the overall revenue range at a location
- Predict category-wise split of anticipated revenue
The Outcome:
To date, with GeoIQ’s recommended locations and revenue prediction model, the brand has opened 35 new successful locations.
About the client:
A renowned wellness and beauty services provider with a strong presence across 100+ cities in India. The brand offers a wide range of services, including slimming, beauty, and dermatological solutions, alongside a diverse portfolio of personal care products. Known for its commitment to holistic well-being, the company operates through a mix of physical locations. This case study will explore how data-driven insights helped the brand locate sites with maximum revenue potential. Also, predicting category-wise revenue potential in each zone for ideal product/service mix and strategy for different regions or cities.
Methodology:
Step 1:
Assess the revenue data of all the current existing stores considering the store area/size. Bifurcate the existing stores into four zones: North, East, South, West.
Step 2:
Identify catchment attributes defining their successful locations as compared to the non-successful ones.
Step 3:
Create models to predict overall store revenue and potential revenue for each product/service category at new recommended locations in each zone.
Step 4:
Recommend locations for new stores with the highest revenue potential in non-cannibalized markets.
Insights:
The recommended high-potential locations were characterized by the following:
Performance defining indicators:
- Spend Indicators: such as spending on restaurants in the catchment, high-end brand presence
- Presence of complimentary outlets: such as density of salons, cosmetic stores, supermarkets
- Target Audience: such as density of high-income households in the catchment, working women population
- Market Characteristics: Density of brands nearby, Higher commercial rentals, high footfall for adjunct brands
Conclusion:
GeoIQ‘s data-driven location recommendations and revenue prediction model successfully empowered the wellness and beauty brand to strategically expand their footprint across high-potential cities and micro-markets. By identifying 100 optimal locations with strong monthly revenue potential the brand has already opened 35 new stores. This data-backed approach not only minimized risk but also ensured efficient resource allocation for their next phase of expansion.
